CSV Cleaner

Automatically detect and fix data quality issues in CSV files. One command, clean data.

Quick Start

python csv_cleaner.py messy_data.csv -o clean_data.csv

No external dependencies. Python 3.10+ only.

What It Fixes

• Duplicate rows — exact match removal
• Column names — standardized to snake_case
• Whitespace — leading/trailing spaces, double spaces
• Null variants — NULL, N/A, None, -, nan, #N/A → normalized
• Date formats — auto-detects 9+ formats, normalizes to ISO 8601
• Encoding — auto-detects UTF-8, Latin-1, Shift-JIS, etc.
• Type detection — infers integer, float, date, boolean, string
• Outlier detection — IQR-based outlier identification

Example

$ python csv_cleaner.py sales_export.csv -o sales_clean.csv

CSV Cleaner — Processing sales_export.csv
  Read 1,247 rows, 12 columns

  Rows:    1,247 → 1,198 (49 duplicates removed)
  Columns: 12 → 12

  Changes:
    Whitespace fixed:    156
    Dates normalized:    89
    Duplicates removed:  49

  Column Profiles:
    customer_name: string, 892 unique
    email: string, 1,043 unique ⚠ 3% null
    signup_date: date, 365 unique
    revenue: float, 1,102 unique ⚠ 4 outliers
    status: string, 3 unique

Options

# Profile only (analyze without modifying)
python csv_cleaner.py input.csv --profile-only

# Keep original column names
python csv_cleaner.py input.csv --keep-names

# Save detailed JSON report
python csv_cleaner.py input.csv -o clean.csv --report report.json

Pro Version

The CSV Cleaner Pro ($19) adds:

• Smart fill for missing values (mean, median, mode, forward-fill)
• Fuzzy deduplication (find similar-but-not-identical rows)
• Outlier handling (flag, cap, or remove)
• Custom validation rules (regex, ranges, required fields)
• Batch processing (clean entire directories)
• Column splitting and merging
• Beautiful HTML reports
• Excel (.xlsx) support

Requirements

• Python 3.10+
• No external dependencies

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License

MIT — free for personal and commercial use.